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PotNet: Pothole detection for autonomous vehicle system using convolutional neural network

ELECTRONICS LETTERS 2021年第2期57卷 53-56页

作者： Dewangan, Deepak Kumar Sahu, Satya Prakash Natl Inst Technol Dept Informat Technol Raipur Madhya Pradesh India

Advancement in vision-based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non-uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F-measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state-of-art techniques.

关键词： Optical, image and video signal processing Road-traffic system control Mobile robots computer vision and image processing techniques Control engineering computing Traffic engineering computing Neural nets

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Haze removal network using unified function for image dehazing

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ELECTRONICS LETTERS 2021年第1期57卷 16-20页

作者： Oh, Hyungseok Kim, Hyena Kim, Yejin Yim, Changhoon Konkuk Univ Dept Comp Sci & Engn Seoul South Korea

The atmospheric scattering model includes two crucial parameters for dehazing: global atmospheric light and the transmission map. Most previous dehazing methods need to obtain these two parameters separately, which makes dehazing a difficult and ill-posed problem. Here, a new unified function that includes both the crucial parameters for haze removal is proposed. Then the haze removal network, which now needs to learn only one function during training, is proposed. image dehazing can be performed as a simple addition of the haze removal function with the input hazy image. Experimental results show that the proposed method gives better subjective results for indoor and outdoor synthesized images as well as natural images compared to previous state-of-the-art methods. Quantitative evaluation results show that the proposed haze removal network gives improved objective results compared with the same previous methods.

关键词： Optical, image and video signal processing computer vision and image processing techniques Neural nets

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Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network

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JOURNAL OF ENGINEERING-JOE 2021年第3期2021卷 125-134页

作者： Sarp, Salih Kuzlu, Murat Pipattanasomporn, Manisa Guler, Ozgur Old Dominion Univ Norfolk Batten Coll Engn & Technol Norfolk VA USA Chulalongkorn Univ Smart Grid Res Unit Bangkok Thailand EKare Inc Fairfax VA USA

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs.

关键词： computer vision and image processing techniques simultaneous wound border segmentation chronic wound datasets training images generative adversarial network applications wounds medical image synthesis Biology and medical computing proper chronic wound treatment plan learning (artificial intelligence) image segmentation medical image processing Biomedical measurement and imaging GAN algorithm Optical, image and video signal processing tissue classification method utilising conditional GAN patient treatment biological tissues Patient care and treatment Patient diagnostic methods and instrumentation GAN model performs hybrid wound border segmentation tissue classification tasks Interpolation and function approximation (numerical analysis) image classification conditional generative adversarial network wound tissue classification Other topics in statistics

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Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

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ELECTRONICS LETTERS 2021年第1期57卷 20-22页

作者： Zhuang, Zhongjie Pan, Jeng-Shyang Chu, Shu-Chuan Luo, Hao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Flinders Univ S Australia Coll Sci & Engn Clovelly Pk SA Australia Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou Peoples R China

Nearest feature line is an effective classification algorithm. However, if a test sample cannot be linear represented by the training samples, the algorithm may not work very well. Moreover, another problem is that it will have a large computation complexity. Therefore, the authors propose a novel algorithm. To begin with, the test sample is linear represented by all the training samples, and the errors between the test sample and every training sample are calculated. The authors only keep the training samples with small errors. In this way, on the one hand, training samples are not suitable for the test sample will be ignored, on the other hand, running time can be reduced. To further reduce the computing time of the algorithm, nearest neighbour search technique is applied to the algorithm. Experiments on numerical and image database show the algorithm cannot only improve the classification accuracy, but also reduce runtime.

关键词： image recognition Optimisation techniques Computational complexity computer vision and image processing techniques Other topics in statistics Combinatorial mathematics Optimisation techniques Other topics in statistics Combinatorial mathematics Machine learning (artificial intelligence) Spatial and pictorial databases

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Fusion of semantic and appearance features for loop-closure detection in a dynamic environment

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ELECTRONICS LETTERS 2021年第2期57卷 56-58页

作者： Xu, Yan Huang, Jiani Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

Loop-closure detection is important in large-scale localisation system. However, it is still difficult in a dynamic environment. An online fast loop-closure detection algorithm based on Deeplabv3 with MobileNetV2 (DpMn2) as network backbone and local difference binary descriptor is proposed, and the algorithm is named as DpMn2-LDB. DpMn2 splits out common dynamic objects of images, and then uses visual geometry group network (VGG16) that is trained on place-centric data to extract global features for nearest neighbour image retrieval. The loop-closure matches are verified based on LDB descriptors and random sample consensus (RANSAC). Experimental results show that the proposed method can obtain a higher recall rate under 100% precision with less execution time per frame on several public datasets compared with other typical or state-of-the-art algorithms.

关键词： image recognition Interpolation and function approximation (numerical analysis) computer vision and image processing techniques Other topics in statistics Combinatorial mathematics Interpolation and function approximation (numerical analysis) Other topics in statistics Combinatorial mathematics Information retrieval techniques Other learning models (inc. Naive Bayes)

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image semantic segmentation method based on GAN network and ERFNet model

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JOURNAL OF ENGINEERING-JOE 2021年第4期2021卷 189-200页

作者： Dong, Chaoxian Sanmenxia Polytech Coll Informat Media Sanmenxia Henan Peoples R China

This article addresses the problems of traditional methods in image semantic segmentation, such as insufficient segmentation of small-scale targets and weak anti-noise ability. A method of image semantic segmentation using a generative adversarial network (GAN) combined with ERFNet model is proposed. First, the asymmetric residual module (ARM) and weak bottleneck module are used to improve the ERFNet network model. Moreover, dilated convolution is used to reduce information loss and improve the performance of small target image semantic segmentation. Then, a U-shaped network is used to improve the generator of GAN to avoid low-level information sharing. In addition, the residual module is introduced into convolution layer to realise the dynamic adjustment of generator weight. Finally, the improved ERFNet model is used as the input of the generator to output the segmented image. It is input to the discriminator together with the label for judgment, which further improves the performance of image semantic segmentation. The proposed method is demonstrated experimentally based on the PyTorch platform. The results show that mean pixel accuracy and mean intersection over the union of the proposed method on the CamVid and Cityscapes datasets are higher than those of other comparisons. In addition, the execution time is short, and the overall image semantic segmentation performance is relatively ideal.

关键词： image semantic segmentation method computer vision and image processing techniques ARM image segmentation Cityscapes datasets Optical, image and video signal processing CamVid datasets asymmetric residual module U neural nets ERFNet network model weak bottleneck module generative adversarial network image classification insufficient segmentation U-shaped network GAN network PyTorch platform Other topics in statistics

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A benchmark of dynamic versus static methods for facial action unit detection

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JOURNAL OF ENGINEERING-JOE 2021年第5期2021卷 252-266页

作者： Alharbawee, L. Pugeault, N. Univ Exeter Coll Engn Math & Phys Sci Exeter Devon England Univ Mosul Stat & Informat Dept Coll Comp Sci & Math Mosul Iraq Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland

Action Units activation is a set of local individual facial muscle parts that occur in time constituting a natural facial expression event. AUs occurrence activation detection can be inferred as temporally consecutive evolving movements of these parts. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. Our work is divided into three contributions: first, we extracted the features from Local Binary Patterns, Local Phase Quantisation, and dynamic texture descriptor LPQTOP with two distinct leveraged network models from different CNN architectures for local deep visual learning for AU image analysis. Second, cascading the LPQTOP feature vector with Long Short-Term Memory is used for coding longer term temporal information. Next, we discovered the importance of stacking LSTM on top of CNN for learning temporal information in combining the spatially and temporally schemes simultaneously. Also, we hypothesised that using an unsupervised Slow Feature Analysis method is able to leach invariant information from dynamic textures. Third, we compared continuous scoring predictions between LPQTOP and SVM, LPQTOP with LSTM, and AlexNet. A competitive substantial performance evaluation was carried out on the Enhanced CK dataset. Overall, the results indicate that CNN is very promising and surpassed all other methods

关键词： computer vision and image processing techniques distinct leveraged network models Long Short-Term Memory detecting AUs learning (artificial intelligence) AUs occurrence activation detection Local Binary Patterns face recognition LPQTOP feature vector muscle temporally consecutive evolving movements longer term temporal information local deep visual learning Optical, image and video signal processing explicit benefits LSTM feature extraction natural facial expression event dynamic texture descriptor LPQTOP static features facial action unit detection unsupervised Slow Feature Analysis method dynamic versus static methods dynamic facial features Local Phase Quantisation image texture invariant information local individual facial muscle parts AU image analysis neural nets unsupervised learning Action Units activation image recognition dynamic textures image classification different CNN architectures support vector machines Other topics in statistics

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image processing and machine learning-based classification method for hyperspectral images

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JOURNAL OF ENGINEERING-JOE 2021年第2期2021卷 85-96页

作者： Yaman, Orhan Yetis, Hasan Karakose, Mehmet Firat Univ Dept Comp Technol TR-23119 Elazig Turkey Firat Univ Dept Comp Engn Elazig Turkey

Hyperspectral images are used to produce effective solutions in many areas due to the number of information they have. However, the number of information they provide may not bring an advantage always, in contrast, it may cause confusion sometimes. In this study, we propose a machine learning-based classification method on the reduced bands using median and mean filters. The proposed method is tested on Indian Pines, Pavia University and Salinas datasets. After applying normalization, median filter and mathematical morphology for each band in an image, a feature matrix with only one band is achieved. Then, the most significant features are selected by using relief feature selection algorithm. The selected features are used for classification with support vector machine and K nearest neighbourhood. For all methods and datasets, a success rate above 99% in terms of accuracy is achieved. The proposed method has two significant contributions. First, when the proposed method is compared with the similar studies in the literature, it is clearly seen that the features selected by relief algorithm significantly increase the success rate of classification algorithms. Second significant contribution of using relief algorithm is obtaining faster methods by reducing the number of hyperspectral bands.

关键词： Probability theory, stochastic processes, and statistics Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Other topics in statistics Optical, image and video signal processing Geophysical techniques and equipment Other topics in statistics computer vision and image processing techniques Geophysics computing Machine learning (artificial intelligence) Support vector machines

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image semantic segmentation method based on GAN network and ENet model

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JOURNAL OF ENGINEERING-JOE 2021年第10期2021卷 594-604页

作者： Li, Huiyi Hunan Mass Media Vocat & Tech Coll Changsha Hunan Peoples R China

Currently, image semantic segmentation has problems such as low accuracy and long running time. This paper proposes an image semantic segmentation method based on generative adversarial network and ENet model combined with deep neural network. This method first improves the network model of generative adversarial network. Ensure the high resolution of the generated image and achieve high similarity with the real image. While ensuring the high accuracy of image semantic segmentation, it effectively improves the real-time performance of network processing. The proposed method is verified based on public data sets. The experimental results show that the segmentation accuracy of this method can reach more than 93%, and the simulation running time is less than 0.171 s, which shows good high accuracy and high real-time performance. A feasible strategy is proposed for the further productization of semantic segmentation.

关键词： image semantic segmentation method computer vision and image processing techniques deep neural network GAN network deep learning (artificial intelligence) Optical, image and video signal processing generative adversarial network network processing network model image segmentation real-time performance ENet model

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A multi-modal heterogeneous data mining algorithm using federated learning

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JOURNAL OF ENGINEERING-JOE 2021年第8期2021卷 458-466页

作者： Wei, Xianyong Shangqiu Polytech Dept Comp Shangqiu 476000 Henan Peoples R China

In disease diagnosis, the classification accuracy based on multi-modal models is usually higher than single-modal models. However, in the process of multimodal data fusion, how to reasonably solve the heterogeneity problem and better extract the information between different modal data has attracted the attention of scholars. Federated learning is an efficient machine learning method that can expand between multiple parameters or multiple computing nodes. It has been applied successfully in the financial industry and cross-industry cooperation. In this paper, a novel algorithm to disease diagnosis model based on federated learning is proposed. The model not only cleverly solves heterogeneous problems, but also excavates information between different modal data to promote the model to be more robust and discriminative. The experiment results show that the proposed model has better performance than traditional fusion algorithms. Notably, compared with other models, this model converges faster and requires less computation.

关键词： Other topics in statistics computer vision and image processing techniques Data handling techniques Biology and medical computing

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